import ipywidgets as widgets
import sys
from pathlib import Path
import os
import importlib


module_path='preprocessing/day_intervals_preproc'
if module_path not in sys.path:
    sys.path.append(module_path)

module_path='utils'
if module_path not in sys.path:
    sys.path.append(module_path)
    
module_path='preprocessing/hosp_module_preproc'
if module_path not in sys.path:
    sys.path.append(module_path)
    
module_path='model'
if module_path not in sys.path:
    sys.path.append(module_path)
#print(sys.path)
root_dir = os.getcwd()
import day_intervals_cohort
from day_intervals_cohort import *

import day_intervals_cohort_v2
from day_intervals_cohort_v2 import *

import data_generation_icu

import data_generation
import evaluation

import feature_selection_hosp
from feature_selection_hosp import *

# import train
# from train import *


import ml_models
from ml_models import *

import dl_train
from dl_train import *

import tokenization
from tokenization import *


import behrt_train
from behrt_train import *

import feature_selection_icu
from feature_selection_icu import *
import fairness
import callibrate_output

importlib.reload(day_intervals_cohort)
import day_intervals_cohort
from day_intervals_cohort import *

importlib.reload(day_intervals_cohort_v2)
import day_intervals_cohort_v2
from day_intervals_cohort_v2 import *

importlib.reload(data_generation_icu)
import data_generation_icu
importlib.reload(data_generation)
import data_generation

importlib.reload(feature_selection_hosp)
import feature_selection_hosp
from feature_selection_hosp import *

importlib.reload(feature_selection_icu)
import feature_selection_icu
from feature_selection_icu import *

importlib.reload(tokenization)
import tokenization
from tokenization import *

importlib.reload(ml_models)
import ml_models
from ml_models import *

importlib.reload(dl_train)
import dl_train
from dl_train import *

importlib.reload(behrt_train)
import behrt_train
from behrt_train import *

importlib.reload(fairness)
import fairness

importlib.reload(callibrate_output)
import callibrate_output

importlib.reload(evaluation)
import evaluation

Welcome to your MIMIC-IV Project

This repository explains the steps to download and clean MIMIC-IV dataset for analysis. The repository is compatible with MIMIC-IV v1.0 and MIMIC-IV v2.0

Please go to:

• https://physionet.org/content/mimiciv/1.0/ for v1.0
• https://physionet.org/content/mimiciv/2.0/ for v2.0

Follow instructions to get access to MIMIC-IV dataset.

Download the files using your terminal:

• wget -r -N -c -np --user mehakg --ask-password https://physionet.org/files/mimiciv/1.0/ or
• wget -r -N -c -np --user mehakg --ask-password https://physionet.org/files/mimiciv/2.0/

Save downloaded files in the parent directory of this github repo.

The structure should look like below for v1.0-

• mimiciv/1.0/core
• mimiciv/1.0/hosp
• mimiciv/1.0/icu

The structure should look like below for v2.0-

• mimiciv/2.0/hosp
• mimiciv/2.0/icu

1. DATA EXTRACTION

Please run below cell to select option for cohort selection. The cohort will be svaed in ./data/cohort/

print("Please select the approriate version of MIMIC-IV for which you have downloaded data ?")
version = widgets.RadioButtons(options=['Version 1','Version 2'],value='Version 2')
display(version)

print("Please select what prediction task you want to perform ?")
radio_input4 = widgets.RadioButtons(options=['Mortality','Length of Stay','Readmission','Phenotype'],value='Mortality')
display(radio_input4)

Please select the approriate version of MIMIC-IV for which you have downloaded data ?

RadioButtons(index=1, options=('Version 1', 'Version 2'), value='Version 2')

Please select what prediction task you want to perform ?

RadioButtons(options=('Mortality', 'Length of Stay', 'Readmission', 'Phenotype'), value='Mortality')

Refining Cohort and Prediction Task Definition

Based on your current selection following block will provide option to further refine prediction task and cohort associated with it:

• First you will refine the prediction task choosing from following options -

  • length of Stay - You can select from two predefined options or enter custom number of days to predict length os stay greater than number of days.

  • Readmission - You can select from two predefined options or enter custom number of days to predict readmission after "number of days" after previous admission.

  • Phenotype Prediction - You can select from four major chronic diseases to predict its future outcome

    • Heart failure
    • CAD (Coronary Artery Disease)
    • CKD (Chronic Kidney Disease)
    • COPD (Chronic obstructive pulmonary disease)

• Second, you will choode whether to perfom above task using ICU or non-ICU admissions data

• Third, you can refine the refine the cohort selection for any of the above choosen prediction tasks by including the admission samples admitted with particular chronic disease -

  • Heart failure
  • CAD (Coronary Artery Disease)
  • CKD (Chronic Kidney Disease)
  • COPD (Chronic obstructive pulmonary disease)

print("Please run below cell to extract the cohort for selected options")

if radio_input4.value=='Length of Stay':
    radio_input2 = widgets.RadioButtons(options=['Length of Stay ge 3','Length of Stay ge 7','Custom'],value='Length of Stay ge 3')
    display(radio_input2)
    text1=widgets.IntSlider(
    value=3,
    min=1,
    max=10,
    step=1,
    disabled=False,
    continuous_update=False,
    orientation='horizontal',
    readout=True,
    readout_format='d'
)
    display(widgets.HBox([widgets.Label('Length of stay ge (in days)',layout={'width': '180px'}), text1]))
elif radio_input4.value=='Readmission':
    radio_input2 = widgets.RadioButtons(options=['30 Day Readmission','60 Day Readmission','90 Day Readmission','120 Day Readmission','Custom'],value='30 Day Readmission')
    display(radio_input2)
    text1=widgets.IntSlider(
    value=30,
    min=10,
    max=150,
    step=10,
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Readmission after (in days)',layout={'width': '180px'}), text1]))
elif radio_input4.value=='Phenotype':
    radio_input2 = widgets.RadioButtons(options=['Heart Failure in 30 days','CAD in 30 days','CKD in 30 days','COPD in 30 days'],value='Heart Failure in 30 days')
    display(radio_input2)
elif radio_input4.value=='Mortality':
    radio_input2 = widgets.RadioButtons(options=['Mortality'],value='Mortality')
    #display(radio_input2)

print("Extract Data")
print("Please select below if you want to work with ICU or Non-ICU data ?")
radio_input1 = widgets.RadioButtons(options=['ICU', 'Non-ICU'],value='ICU')
display(radio_input1)

print("Please select if you want to perform choosen prediction task for a specific disease.")
radio_input3 = widgets.RadioButtons(options=['No Disease Filter','Heart Failure','CKD','CAD','COPD'],value='CKD')
display(radio_input3)

Extract Data
Please select below if you want to work with ICU or Non-ICU data ?

RadioButtons(options=('ICU', 'Non-ICU'), value='ICU')

Please select if you want to perform choosen prediction task for a specific disease.

RadioButtons(index=2, options=('No Disease Filter', 'Heart Failure', 'CKD', 'CAD', 'COPD'), value='CKD')

disease_label=""
time=0
label=radio_input4.value

if label=='Readmission':
    if radio_input2.value=='Custom':
        time=text1.value
    else:
        time=int(radio_input2.value.split()[0])
elif label=='Length of Stay':
    if radio_input2.value=='Custom':
        time=text1.value
    else:
        time=int(radio_input2.value.split()[4])

if label=='Phenotype':    
    if radio_input2.value=='Heart Failure in 30 days':
        label='Readmission'
        time=30
        disease_label='I50'
    elif radio_input2.value=='CAD in 30 days':
        label='Readmission'
        time=30
        disease_label='I25'
    elif radio_input2.value=='CKD in 30 days':
        label='Readmission'
        time=30
        disease_label='N18'
    elif radio_input2.value=='COPD in 30 days':
        label='Readmission'
        time=30
        disease_label='J44'
    
data_icu=radio_input1.value=="ICU"
data_mort=label=="Mortality"
data_admn=label=='Readmission'
data_los=label=='Length of Stay'
        

if (radio_input3.value=="Heart Failure"):
    icd_code='I50'
elif (radio_input3.value=="CKD"):
    icd_code='N18'
elif (radio_input3.value=="COPD"):
    icd_code='J44'
elif (radio_input3.value=="CAD"):
    icd_code='I25'
else:
    icd_code='No Disease Filter'

if version.value=='Version 1':
    version_path="mimiciv/1.0"
    cohort_output = day_intervals_cohort.extract_data(radio_input1.value,label,time,icd_code, root_dir,disease_label)
elif version.value=='Version 2':
    version_path="mimiciv/2.0"
    cohort_output = day_intervals_cohort_v2.extract_data(radio_input1.value,label,time,icd_code, root_dir,disease_label)

===========MIMIC-IV v2.0============
EXTRACTING FOR: | ICU | MORTALITY | ADMITTED DUE TO N18 | 0 |
[ MORTALITY LABELS FINISHED ]
[ COHORT SUCCESSFULLY SAVED ]
[ SUMMARY SUCCESSFULLY SAVED ]
Mortality FOR ICU DATA
# Admission Records: 16390
# Patients: 10276
# Positive cases: 1481
# Negative cases: 14909

2. FEATURE SELECTION

Features available for ICU data -

• Diagnosis (https://mimic.mit.edu/docs/iv/modules/hosp/diagnoses_icd/)
• Procedures (https://mimic.mit.edu/docs/iv/modules/icu/procedureevents/)
• Medications (https://mimic.mit.edu/docs/iv/modules/icu/inputevents/)
• Output Events (https://mimic.mit.edu/docs/iv/modules/icu/outputevents/)
• Chart Events (https://mimic.mit.edu/docs/iv/modules/icu/chartevents/)

Features available for ICU data -

• Diagnosis (https://mimic.mit.edu/docs/iv/modules/hosp/diagnoses_icd/)
• Procedures (https://mimic.mit.edu/docs/iv/modules/hosp/procedures_icd/)
• Medications (https://mimic.mit.edu/docs/iv/modules/hosp/prescriptions/)
• Lab Events (https://mimic.mit.edu/docs/iv/modules/hosp/labevents/)

All features will be saved in ./data/features/

Please run below cell to select features

print("Feature Selection")
if data_icu:
    print("Which Features you want to include for cohort?")
    check_input1 = widgets.Checkbox(description='Diagnosis', value=True)
    display(check_input1)
    check_input2 = widgets.Checkbox(description='Output Events')
    display(check_input2)
    check_input3 = widgets.Checkbox(description='Chart Events(Labs and Vitals)', value=True)
    display(check_input3)
    check_input4 = widgets.Checkbox(description='Procedures')
    display(check_input4)
    check_input5 = widgets.Checkbox(description='Medications', value=True)
    display(check_input5)
else:
    print("Which Features you want to include for cohort?")
    check_input1 = widgets.Checkbox(description='Diagnosis')
    display(check_input1)
    check_input2 = widgets.Checkbox(description='Labs')
    display(check_input2)
    check_input3 = widgets.Checkbox(description='Procedures')
    display(check_input3)
    check_input4 = widgets.Checkbox(description='Medications')
    display(check_input4)
print("**Please run below cell to extract selected features**")

Feature Selection
Which Features you want to include for cohort?

Checkbox(value=True, description='Diagnosis')

Checkbox(value=False, description='Output Events')

Checkbox(value=True, description='Chart Events(Labs and Vitals)')

Checkbox(value=False, description='Procedures')

Checkbox(value=True, description='Medications')

**Please run below cell to extract selected features**

if data_icu:
    diag_flag=check_input1.value
    out_flag=check_input2.value
    chart_flag=check_input3.value
    proc_flag=check_input4.value
    med_flag=check_input5.value
    feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
else:
    diag_flag=check_input1.value
    lab_flag=check_input2.value
    proc_flag=check_input3.value
    med_flag=check_input4.value
    feature_nonicu(cohort_output, version_path,diag_flag,lab_flag,proc_flag,med_flag)

[EXTRACTING DIAGNOSIS DATA]

df=pd.read_csv(root_dir+'/data/cohort/cohort_icu_mortality_0__N18.csv.gz',compression='gzip')
unique_values = {column: df[column].unique() for column in df.columns}
print(unique_values)

{'subject_id': array([10000980, 10002155, 10002430, ..., 19997473, 19998330, 19999625]), 'stay_id': array([39765666, 33685454, 31090461, ..., 33428243, 31199714, 31070865]), 'intime': array(['2189-06-27 08:42:00', '2129-08-04 12:45:00',
       '2130-09-24 00:50:00', ..., '2178-11-27 22:53:00',
       '2178-11-29 21:51:19', '2139-10-10 19:18:00'], dtype=object), 'outtime': array(['2189-06-27 20:38:27', '2129-08-10 17:02:38',
       '2130-09-27 22:13:41', ..., '2178-11-29 21:29:39',
       '2178-11-29 22:44:27', '2139-10-11 18:21:28'], dtype=object), 'Age': array([73, 80, 86, 72, 47, 82, 51, 66, 69, 89, 91, 70, 54, 77, 53, 87, 67,
       65, 83, 50, 58, 57, 78, 60, 63, 46, 64, 79, 88, 55, 62, 76, 85, 75,
       48, 84, 68, 40, 59, 34, 56, 71, 35, 74, 49, 44, 81, 41, 61, 45, 38,
       32, 37, 43, 25, 42, 30, 27, 33, 31, 52, 39, 23, 36, 28, 26, 20, 24,
       29, 22, 21]), 'gender': array(['F', 'M'], dtype=object), 'ethnicity': array(['BLACK/AFRICAN AMERICAN', 'WHITE', 'BLACK/CAPE VERDEAN', 'UNKNOWN',
       'ASIAN - SOUTH EAST ASIAN', 'BLACK/AFRICAN', 'PORTUGUESE',
       'HISPANIC/LATINO - CUBAN', 'OTHER',
       'HISPANIC/LATINO - PUERTO RICAN', 'WHITE - RUSSIAN',
       'ASIAN - ASIAN INDIAN', 'WHITE - EASTERN EUROPEAN',
       'ASIAN - CHINESE', 'ASIAN', 'HISPANIC OR LATINO',
       'UNABLE TO OBTAIN', 'WHITE - OTHER EUROPEAN',
       'PATIENT DECLINED TO ANSWER',
       'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER',
       'AMERICAN INDIAN/ALASKA NATIVE',
       'HISPANIC/LATINO - CENTRAL AMERICAN', 'BLACK/CARIBBEAN ISLAND',
       'HISPANIC/LATINO - GUATEMALAN', 'HISPANIC/LATINO - DOMINICAN',
       'HISPANIC/LATINO - HONDURAN', 'HISPANIC/LATINO - SALVADORAN',
       'HISPANIC/LATINO - COLUMBIAN', 'ASIAN - KOREAN',
       'WHITE - BRAZILIAN', 'SOUTH AMERICAN', 'HISPANIC/LATINO - MEXICAN',
       'MULTIPLE RACE/ETHNICITY'], dtype=object), 'insurance': array(['Medicare', 'Other', 'Medicaid'], dtype=object), 'label': array([0, 1]), 'dod': array(['2193-08-26 00:00:00', '2131-03-10 00:00:00',
       '2130-01-11 00:00:00', ..., '2185-11-17 00:00:00',
       '2124-02-20 00:00:00', '2178-12-08 00:00:00'], dtype=object), 'hadm_id': array([26913865, 23822395, 28994087, ..., 21135114, 24096838, 25304202])}

3. CLINICAL GROUPING

Below you will have option to clinically group diagnosis and medications. Grouping medical codes will reduce dimensional space of features.

Default options selected below will group medical codes to reduce feature dimension space.

Please run below cell to select preprocessing for diferent features

if data_icu:
    if diag_flag:
        print("Do you want to group ICD 10 DIAG codes ?")
        radio_input4 = widgets.RadioButtons(options=['Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes','Convert ICD-9 to ICD-10 and group ICD-10 codes'],value='Convert ICD-9 to ICD-10 and group ICD-10 codes',layout={'width': '100%'})
        display(radio_input4)   
    
else:
    if diag_flag:
        print("Do you want to group ICD 10 DIAG codes ?")
        radio_input4 = widgets.RadioButtons(options=['Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes','Convert ICD-9 to ICD-10 and group ICD-10 codes'],value='Convert ICD-9 to ICD-10 and group ICD-10 codes',layout={'width': '100%'})
        display(radio_input4)     
    if med_flag:
        print("Do you want to group Medication codes to use Non propietary names?")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='Yes',layout={'width': '100%'})
        display(radio_input5)
    if proc_flag:
        print("Which ICD codes for Procedures you want to keep in data?")
        radio_input6 = widgets.RadioButtons(options=['ICD-9 and ICD-10','ICD-10'],value='ICD-10',layout={'width': '100%'})
        display(radio_input6)
print("**Please run below cell to perform feature preprocessing**")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[9], line 2
      1 if data_icu:
----> 2     if diag_flag:
      3         print("Do you want to group ICD 10 DIAG codes ?")
      4         radio_input4 = widgets.RadioButtons(options=['Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes','Convert ICD-9 to ICD-10 and group ICD-10 codes'],value='Convert ICD-9 to ICD-10 and group ICD-10 codes',layout={'width': '100%'})

NameError: name 'diag_flag' is not defined

group_diag=False
group_med=False
group_proc=False
if data_icu:
    if diag_flag:
        group_diag=radio_input4.value
    preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
else:
    if diag_flag:
        group_diag=radio_input4.value
    if med_flag:
        group_med=radio_input5.value
    if proc_flag:
        group_proc=radio_input6.value
    preprocess_features_hosp(cohort_output, diag_flag,proc_flag,med_flag,False,group_diag,group_med,group_proc,False,False,0,0)

[PROCESSING DIAGNOSIS DATA]
Total number of rows 362675
[SUCCESSFULLY SAVED DIAGNOSIS DATA]

4. SUMMARY OF FEATURES

This step will generate summary of all features extracted so far.
It will save summary files in ./data/summary/

• These files provide summary about mean frequency of medical codes per admission.
  
• It also provides total occurrence count of each medical code.
  
• For labs and chart events it will also provide
  missing % which tells how many rows for a certain medical code has missing value.

Please use this information to further refine your cohort by selecting
which medical codes in each feature you want to keep and
which codes you would like to remove for downstream analysis tasks.

Please run below cell to generate summary files

if data_icu:
    generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
else:
    generate_summary_hosp(diag_flag,proc_flag,med_flag,lab_flag)

[GENERATING FEATURE SUMMARY]
[SUCCESSFULLY SAVED FEATURE SUMMARY]

5. Feature Selection

based on the files generated in previous step and other infromation gathered by you,
Please select which medical codes you want to include in this study.

Please run below cell to to select options for which features you want to perform feature selection.

• Select Yes if you want to select a subset of medical codes for that feature and
  edit the corresponding feature file for it.
• Select No if you want to keep all the codes in a feature.

if data_icu:
    if diag_flag:
        print("Do you want to do Feature Selection for Diagnosis \n (If yes, please edit list of codes in ./data/summary/diag_features.csv)")
        radio_input4 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input4)       
    if med_flag:
        print("Do you want to do Feature Selection for Medication \n (If yes, please edit list of codes in ./data/summary/med_features.csv)")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input5)   
    if proc_flag:
        print("Do you want to do Feature Selection for Procedures \n (If yes, please edit list of codes in ./data/summary/proc_features.csv)")
        radio_input6 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input6)   
    if out_flag:
        print("Do you want to do Feature Selection for Output event \n (If yes, please edit list of codes in ./data/summary/out_features.csv)")
        radio_input7 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input7)  
    if chart_flag:
        print("Do you want to do Feature Selection for Chart events \n (If yes, please edit list of codes in ./data/summary/chart_features.csv)")
        radio_input8 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input8)  
else:
    if diag_flag:
        print("Do you want to do Feature Selection for Diagnosis \n (If yes, please edit list of codes in ./data/summary/diag_features.csv)")
        radio_input4 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input4)         
    if med_flag:
        print("Do you want to do Feature Selection for Medication \n (If yes, please edit list of codes in ./data/summary/med_features.csv)")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input5)   
    if proc_flag:
        print("Do you want to do Feature Selection for Procedures \n (If yes, please edit list of codes in ./data/summary/proc_features.csv)")
        radio_input6 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input6)   
    if lab_flag:
        print("Do you want to do Feature Selection for Labs \n (If yes, please edit list of codes in ./data/summary/lab_features.csv)")
        radio_input7 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input7)   
print("**Please run below cell to perform feature selection**")

Do you want to do Feature Selection for Diagnosis 
 (If yes, please edit list of codes in ./data/summary/diag_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')

Do you want to do Feature Selection for Medication 
 (If yes, please edit list of codes in ./data/summary/med_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')

Do you want to do Feature Selection for Chart events 
 (If yes, please edit list of codes in ./data/summary/chart_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')

**Please run below cell to perform feature selection**

select_diag=False
select_med=False
select_proc=False
select_lab=False
select_out=False
select_chart=False

if data_icu:
    if diag_flag:
        select_diag=radio_input4.value == 'Yes'
    if med_flag:
        select_med=radio_input5.value == 'Yes'
    if proc_flag:
        select_proc=radio_input6.value == 'Yes'
    if out_flag:
        select_out=radio_input7.value == 'Yes'
    if chart_flag:
        select_chart=radio_input8.value == 'Yes'
    features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
else:
    if diag_flag:
        select_diag=radio_input4.value == 'Yes'
    if med_flag:
        select_med=radio_input5.value == 'Yes'
    if proc_flag:
        select_proc=radio_input6.value == 'Yes'
    if lab_flag:
        select_lab=radio_input7.value == 'Yes'
    features_selection_hosp(cohort_output, diag_flag,proc_flag,med_flag,lab_flag,select_diag,select_med,select_proc,select_lab)

6. CLEANING OF FEATURES

Below you will have option to to clean lab and chart events by performing outlier removal and unit conversion.

Outlier removal is performed to remove values higher than selected right threshold percentile and lower than selected left threshold percentile among all values for each itemid.

Please run below cell to select preprocessing for diferent features

if data_icu:
    if chart_flag:
        print("Outlier removal in values of chart events ?")
        layout = widgets.Layout(width='100%', height='40px') #set width and height

        radio_input5 = widgets.RadioButtons(options=['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'],value='No outlier detection',layout=layout)
        display(radio_input5)
        outlier=widgets.IntSlider(
        value=98,
        min=90,
        max=99,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        left_outlier=widgets.IntSlider(
        value=0,
        min=0,
        max=10,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        #display(oulier)
        display(widgets.HBox([widgets.Label('Right Outlier Threshold',layout={'width': '150px'}), outlier]))
        display(widgets.HBox([widgets.Label('Left Outlier Threshold',layout={'width': '150px'}), left_outlier]))
    
else:      
    if lab_flag:
        print("Outlier removal in values of lab events ?")
        layout = widgets.Layout(width='100%', height='40px') #set width and height

        radio_input7 = widgets.RadioButtons(options=['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'],value='No outlier detection',layout=layout)
        display(radio_input7)
        outlier=widgets.IntSlider(
        value=98,
        min=90,
        max=99,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        left_outlier=widgets.IntSlider(
        value=0,
        min=0,
        max=10,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        #display(oulier)
        display(widgets.HBox([widgets.Label('Right Outlier Threshold',layout={'width': '150px'}), outlier]))
        display(widgets.HBox([widgets.Label('Left Outlier Threshold',layout={'width': '150px'}), left_outlier]))
print("**Please run below cell to perform feature preprocessing**")

Outlier removal in values of chart events ?

RadioButtons(layout=Layout(height='40px', width='100%'), options=('No outlier detection', 'Impute Outlier (def…

HBox(children=(Label(value='Right Outlier Threshold', layout=Layout(width='150px')), IntSlider(value=98, layou…

HBox(children=(Label(value='Left Outlier Threshold', layout=Layout(width='150px')), IntSlider(value=0, layout=…

**Please run below cell to perform feature preprocessing**

thresh=0
if data_icu:
    if chart_flag:
        clean_chart=radio_input5.value!='No outlier detection'
        impute_outlier_chart=radio_input5.value=='Impute Outlier (default:98)'
        thresh=outlier.value
        left_thresh=left_outlier.value
    preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
else:
    if lab_flag:
        clean_lab=radio_input7.value!='No outlier detection'
        impute_outlier=radio_input7.value=='Impute Outlier (default:98)'
        thresh=outlier.value
        left_thresh=left_outlier.value
    preprocess_features_hosp(cohort_output, False,False,False,lab_flag,False,False,False,clean_lab,impute_outlier,thresh,left_thresh)

7. Time-Series Representation

In this section, please choose how you want to process and represent time-series data.

• First option is to select the length of time-series data you want to include for this study. (Default is 72 hours)

• Second option is to select bucket size which tells in what size time windows you want to divide your time-series.
  For example, if you select 2 bucket size, it wil aggregate data for every 2 hours and
  a time-series of length 24 hours will be represented as time-series with 12 time-windows
  where data for every 2 hours is agggregated from original raw time-series.

During this step, we will also save the time-series data in data dictionaries in the format that can be directly used for following deep learning analysis.

Imputation

You can also choose if you want to impute lab/chart values. The imputation will be done by froward fill and mean or median imputation.
Values will be forward fill first and if no value exists for that admission we will use mean or median value for the patient.

The data dictionaries will be saved in ./data/dict/

Please refer the readme to know the structure of data dictionaries.

Please run below cell to select time-series representation

print("=======Time-series Data Represenation=======")

print("Length of data to be included for time-series prediction ?")
if(data_mort):
    radio_input8 = widgets.RadioButtons(options=['First 72 hours','First 48 hours','First 24 hours','Custom'],value='First 72 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=24,
    max=72,
    step=1,
    description='Fisrt',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Fisrt (in hours):',layout={'width': '150px'}), text2]))
elif(data_admn):
    radio_input8 = widgets.RadioButtons(options=['Last 72 hours','Last 48 hours','Last 24 hours','Custom'],value='Last 72 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=24,
    max=72,
    step=1,
    description='Last',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Last (in hours):',layout={'width': '150px'}), text2]))
elif(data_los):
    radio_input8 = widgets.RadioButtons(options=['First 12 hours','First 24 hours','Custom'],value='First 24 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=12,
    max=72,
    step=1,
    description='First',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Fisrt (in hours):',layout={'width': '150px'}), text2]))
    
    
print("What time bucket size you want to choose ?")
radio_input7 = widgets.RadioButtons(options=['1 hour','2 hour','3 hour','4 hour','5 hour','Custom'],value='1 hour')
display(radio_input7)
text1=widgets.IntSlider(
    value=1,
    min=1,
    max=6,
    step=1,
    disabled=False
    )
#display(text1)
display(widgets.HBox([widgets.Label('Bucket Size (in hours):',layout={'width': '150px'}), text1]))
print("Do you want to forward fill and mean or median impute lab/chart values to form continuous data signal?")
radio_impute = widgets.RadioButtons(options=['No Imputation', 'forward fill and mean','forward fill and median'],value='No Imputation')
display(radio_impute)   

radio_input6 = widgets.RadioButtons(options=['0 hours','2 hours','4 hours','6 hours'],value='0 hours')
if(data_mort):
    print("If you have choosen mortality prediction task, then what prediction window length you want to keep?")
    radio_input6 = widgets.RadioButtons(options=['2 hours','4 hours','6 hours','8 hours','Custom'],value='2 hours')
    display(radio_input6)
    text3=widgets.IntSlider(
    value=2,
    min=2,
    max=8,
    step=1,
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Prediction window (in hours)',layout={'width': '180px'}), text3]))
print("**Please run below cell to perform time-series represenation and save in data dictionaries**")

=======Time-series Data Represenation=======
Length of data to be included for time-series prediction ?

RadioButtons(options=('First 72 hours', 'First 48 hours', 'First 24 hours', 'Custom'), value='First 72 hours')

HBox(children=(Label(value='Fisrt (in hours):', layout=Layout(width='150px')), IntSlider(value=72, description…

What time bucket size you want to choose ?

RadioButtons(options=('1 hour', '2 hour', '3 hour', '4 hour', '5 hour', 'Custom'), value='1 hour')

HBox(children=(Label(value='Bucket Size (in hours):', layout=Layout(width='150px')), IntSlider(value=1, max=6,…

Do you want to forward fill and mean or median impute lab/chart values to form continuous data signal?

RadioButtons(options=('No Imputation', 'forward fill and mean', 'forward fill and median'), value='No Imputati…

If you have choosen mortality prediction task, then what prediction window length you want to keep?

RadioButtons(options=('2 hours', '4 hours', '6 hours', '8 hours', 'Custom'), value='2 hours')

HBox(children=(Label(value='Prediction window (in hours)', layout=Layout(width='180px')), IntSlider(value=2, m…

**Please run below cell to perform time-series represenation and save in data dictionaries**

if (radio_input6.value=='Custom'):
    predW=int(text3.value)
else:
    predW=int(radio_input6.value[0].strip())
if (radio_input7.value=='Custom'):
    bucket=int(text1.value)
else:
    bucket=int(radio_input7.value[0].strip())
if (radio_input8.value=='Custom'):
    include=int(text2.value)
else:
    include=int(radio_input8.value.split()[1])
if (radio_impute.value=='forward fill and mean'):
    impute='Mean'
elif (radio_impute.value=='forward fill and median'):
    impute='Median'
else:
    impute=False

if data_icu:
    gen=data_generation_icu.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
    #gen=data_generation_icu.Generator(cohort_output,data_mort,diag_flag,False,False,chart_flag,False,impute,include,bucket,predW)
    #if chart_flag:
    #    gen=data_generation_icu.Generator(cohort_output,data_mort,False,False,False,chart_flag,False,impute,include,bucket,predW)
else:
    gen=data_generation.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,lab_flag,proc_flag,med_flag,impute,include,bucket,predW)

0it [00:00, ?it/s]

[ READ COHORT ]
[ ======READING DIAGNOSIS ]
[ ======READING CHART EVENTS ]

4it [01:27, 21.78s/it]

[ ======READING MEDICATIONS ]
[ READ ALL FEATURES ]
include_time 72
[ PROCESSED TIME SERIES TO EQUAL LENGTH  ]

100%|██████████| 72/72 [00:04<00:00, 17.57it/s]

bucket 1
[ PROCESSED TIME SERIES TO EQUAL TIME INTERVAL ]
72

100%|██████████| 5736/5736 [16:19<00:00,  5.86it/s]

[ SUCCESSFULLY SAVED DATA DICTIONARIES ]

8. Machine Learning Models

Below we provide options to select -

• Type of machine learning model
• Wheteher to concatenate or aggregate time-series features. For example, if the EHR data has collected value for Blood Pressure for one year over 4 time windows of 3 months each then,
  • Conactenate will concatenate all four values resulting in 4 different features for blood pressure,
  • Aggregate will aggreagte(mean) over four tiem windows resulting in one feature for blood pressure.

print("=======Machine :earning Models=======")
radio_input5 = widgets.RadioButtons(options=['Logistic Regression','Random Forest','Gradient Bossting','Xgboost'],value='Gradient Bossting')
display(radio_input5)
print("Do you wnat to conactenate the time-series feature")
radio_input6 = widgets.RadioButtons(options=['Concatenate','Aggregate'],value='Concatenate')
display(radio_input6)
print("Please select below option for cross-validation")
radio_input7 = widgets.RadioButtons(options=['No CV','5-fold CV','10-fold CV'],value='5-fold CV')
display(radio_input7)
print("Do you want to do oversampling for minority calss ?")
radio_input8 = widgets.RadioButtons(options=['True','False'],value='True')
display(radio_input8)

=======Machine :earning Models=======

RadioButtons(index=2, options=('Logistic Regression', 'Random Forest', 'Gradient Bossting', 'Xgboost'), value=…

Do you wnat to conactenate the time-series feature

RadioButtons(options=('Concatenate', 'Aggregate'), value='Concatenate')

Please select below option for cross-validation

RadioButtons(index=1, options=('No CV', '5-fold CV', '10-fold CV'), value='5-fold CV')

Do you want to do oversampling for minority calss ?

RadioButtons(options=('True', 'False'), value='True')

import matplotlib.pyplot as plt

df = pd.read_csv(root_dir+'/ethnicity_data.csv', delimiter=';')
column_names = ['num_patient', 'Ethnicity']
df.columns = column_names
df.plot(x='Ethnicity', y='num_patient', kind='bar', color='royalblue')
plt.xlabel('Ethnicity')
plt.ylabel('Number of Patients')
plt.title('Distribution of Ethnicity')
plt.show()

if radio_input7.value=='No CV':
    cv=0
elif radio_input7.value=='5-fold CV':
    cv=int(5)
elif radio_input7.value=='10-fold CV':
    cv=int(10)

ml=ml_models.ML_models(data_icu,cv,radio_input5.value,concat=radio_input6.value=='Concatenate',oversampling=radio_input8.value=='True')

Total Samples 5736
Positive Samples 854
=============OVERSAMPLING===============
Total Samples 9764
Positive Samples 4882
=================== 0 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[38], line 8
      5 elif radio_input7.value=='10-fold CV':
      6     cv=int(10)
----> 8 ml=ml_models.ML_models(data_icu,cv,radio_input5.value,concat=radio_input6.value=='Concatenate',oversampling=radio_input8.value=='True')

File ~/MIMIC_IV/model/ml_models.py:42, in ML_models.__init__(self, data_icu, k_fold, model_type, concat, oversampling)
     40 self.oversampling=oversampling
     41 self.loss=evaluation.Loss('cuda',True,True,True,True,True,True,True,True,True,True,True)
---> 42 self.ml_train()

File ~/MIMIC_IV/model/ml_models.py:134, in ML_models.ml_train(self)
    131 print(Y_test.shape)
    132 #print("just before training")
    133 #print(X_test.head())
--> 134 self.train_model(X_train,Y_train,X_test,Y_test)

File ~/MIMIC_IV/model/ml_models.py:144, in ML_models.train_model(self, X_train, Y_train, X_test, Y_test)
    142     prob=model.predict_proba(X_test)
    143     logits=np.log2(prob[:,1]/prob[:,0])
--> 144     self.loss(prob[:,1],np.asarray(Y_test),logits,False,True)
    145     self.save_output(Y_test,prob[:,1],logits)
    147 elif self.model_type=='Logistic Regression':

File ~/miniconda3/envs/mimic/lib/python3.8/site-packages/torch/nn/modules/module.py:722, in Module._call_impl(self, *input, **kwargs)
    720     result = self._slow_forward(*input, **kwargs)
    721 else:
--> 722     result = self.forward(*input, **kwargs)
    723 for hook in itertools.chain(
    724         _global_forward_hooks.values(),
    725         self._forward_hooks.values()):
    726     hook_result = hook(self, input, result)

File ~/MIMIC_IV/model/evaluation.py:76, in Loss.forward(self, prob, labels, logits, train, standalone)
     74     pos_prob=pos_prob.to(self.device)
     75     pos_label=pos_label.to(self.device)
---> 76     pos_loss = self.classify_loss(pos_prob, pos_label) 
     78 if len(neg_prob):
     79     neg_prob=neg_prob.to(self.device)

File ~/miniconda3/envs/mimic/lib/python3.8/site-packages/torch/nn/modules/module.py:722, in Module._call_impl(self, *input, **kwargs)
    720     result = self._slow_forward(*input, **kwargs)
    721 else:
--> 722     result = self.forward(*input, **kwargs)
    723 for hook in itertools.chain(
    724         _global_forward_hooks.values(),
    725         self._forward_hooks.values()):
    726     hook_result = hook(self, input, result)

File ~/miniconda3/envs/mimic/lib/python3.8/site-packages/torch/nn/modules/loss.py:529, in BCELoss.forward(self, input, target)
    528 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 529     return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)

File ~/miniconda3/envs/mimic/lib/python3.8/site-packages/torch/nn/functional.py:2483, in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
   2480     new_size = _infer_size(target.size(), weight.size())
   2481     weight = weight.expand(new_size)
-> 2483 return torch._C._nn.binary_cross_entropy(
   2484     input, target, weight, reduction_enum)

RuntimeError: CUDA error: no kernel image is available for execution on the device

9. Deep Learning Models

• Time-series LSTM and Time-series CNN which will only use time-series events like medications, charts, labs, output events to train model.

• Hybrid LSTM and Hybrid CNN will use static data - diagnosis, demographic data aong with other time-series data to train model.

• LSTM with Attention model will use attention layer to rank the important features and learn to predict output. It will use both static and time-series data.

Go to ./model/parameter.py and define all variables needed for model building and training

Please run below cell to select which model to use

radio_input6=widgets.RadioButtons(options=['Time-series LSTM','Time-series CNN','Hybrid LSTM','Hybrid CNN'],value='Time-series LSTM')
display(radio_input6)
print("Please select below option for cross-validation")
radio_input7 = widgets.RadioButtons(options=['No CV','5-fold CV','10-fold CV'],value='5-fold CV')
display(radio_input7)
print("Do you want to do oversampling for minority calss ?")
radio_input8 = widgets.RadioButtons(options=['True','False'],value='True')
display(radio_input8)

RadioButtons(options=('Time-series LSTM', 'Time-series CNN', 'Hybrid LSTM', 'Hybrid CNN'), value='Time-series …

Please select below option for cross-validation

RadioButtons(index=1, options=('No CV', '5-fold CV', '10-fold CV'), value='5-fold CV')

Do you want to do oversampling for minority calss ?

RadioButtons(options=('True', 'False'), value='True')

if radio_input7.value=='No CV':
    cv=0
elif radio_input7.value=='5-fold CV':
    cv=int(5)
elif radio_input7.value=='10-fold CV':
    cv=int(10)
    
if data_icu:
    model=dl_train.DL_models(data_icu,diag_flag,proc_flag,out_flag,chart_flag,med_flag,False,radio_input6.value,cv,oversampling=radio_input8.value=='True',model_name='attn_icu_read',train=True)
else:
    model=dl_train.DL_models(data_icu,diag_flag,proc_flag,False,False,med_flag,lab_flag,radio_input6.value,cv,oversampling=radio_input8.value=='True',model_name='attn_icu_read',train=True)

===============MODEL TRAINING===============
Total Samples 5736
Positive Samples 854
=============OVERSAMPLING===============
Total Samples 9764
Positive Samples 4882
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 0 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.20
AU-ROC: 0.75
AU-PRC: 0.34
AU-PRC Baaseline: 0.13
Accuracy: 0.68
Precision: 0.25
Recall: 0.70
Specificity: 0.68
NPV: 0.94
ECE: 0.31
MCE: 0.46
======= VALIDATION ========
BCE Loss: 1.12
AU-ROC: 0.81
AU-PRC: 0.79
AU-PRC Baaseline: 0.48
Accuracy: 0.75
Precision: 0.75
Recall: 0.72
Specificity: 0.77
NPV: 0.74
ECE: 0.11
MCE: 0.17
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.87
AU-ROC: 0.88
AU-PRC: 0.57
AU-PRC Baaseline: 0.13
Accuracy: 0.80
Precision: 0.38
Recall: 0.82
Specificity: 0.80
NPV: 0.97
ECE: 0.21
MCE: 0.47
======= VALIDATION ========
BCE Loss: 1.13
AU-ROC: 0.81
AU-PRC: 0.78
AU-PRC Baaseline: 0.48
Accuracy: 0.72
Precision: 0.74
Recall: 0.66
Specificity: 0.78
NPV: 0.71
ECE: 0.08
MCE: 0.17
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.75
AU-ROC: 0.91
AU-PRC: 0.61
AU-PRC Baaseline: 0.13
Accuracy: 0.82
Precision: 0.41
Recall: 0.88
Specificity: 0.81
NPV: 0.98
ECE: 0.19
MCE: 0.51
======= VALIDATION ========
BCE Loss: 1.15
AU-ROC: 0.80
AU-PRC: 0.78
AU-PRC Baaseline: 0.48
Accuracy: 0.73
Precision: 0.68
Recall: 0.84
Specificity: 0.63
NPV: 0.80
ECE: 0.06
MCE: 0.13
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.66
AU-ROC: 0.93
AU-PRC: 0.64
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.45
Recall: 0.93
Specificity: 0.83
NPV: 0.99
ECE: 0.16
MCE: 0.41
======= VALIDATION ========
BCE Loss: 1.34
AU-ROC: 0.78
AU-PRC: 0.74
AU-PRC Baaseline: 0.48
Accuracy: 0.72
Precision: 0.71
Recall: 0.74
Specificity: 0.71
NPV: 0.74
ECE: 0.13
MCE: 0.23
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.96
AU-ROC: 0.87
AU-PRC: 0.85
AU-PRC Baaseline: 0.51
Accuracy: 0.79
Precision: 0.76
Recall: 0.88
Specificity: 0.71
NPV: 0.85
ECE: 0.07
MCE: 0.21
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 1 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.20
AU-ROC: 0.75
AU-PRC: 0.33
AU-PRC Baaseline: 0.13
Accuracy: 0.68
Precision: 0.24
Recall: 0.70
Specificity: 0.67
NPV: 0.94
ECE: 0.30
MCE: 0.43
======= VALIDATION ========
BCE Loss: 1.11
AU-ROC: 0.81
AU-PRC: 0.80
AU-PRC Baaseline: 0.50
Accuracy: 0.72
Precision: 0.76
Recall: 0.65
Specificity: 0.80
NPV: 0.69
ECE: 0.07
MCE: 0.20
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.86
AU-ROC: 0.88
AU-PRC: 0.56
AU-PRC Baaseline: 0.13
Accuracy: 0.81
Precision: 0.39
Recall: 0.82
Specificity: 0.80
NPV: 0.97
ECE: 0.22
MCE: 0.45
======= VALIDATION ========
BCE Loss: 1.11
AU-ROC: 0.80
AU-PRC: 0.79
AU-PRC Baaseline: 0.50
Accuracy: 0.72
Precision: 0.73
Recall: 0.68
Specificity: 0.75
NPV: 0.70
ECE: 0.04
MCE: 0.11
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.69
AU-ROC: 0.92
AU-PRC: 0.65
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.44
Recall: 0.86
Specificity: 0.83
NPV: 0.98
ECE: 0.16
MCE: 0.50
======= VALIDATION ========
BCE Loss: 1.22
AU-ROC: 0.79
AU-PRC: 0.77
AU-PRC Baaseline: 0.50
Accuracy: 0.72
Precision: 0.71
Recall: 0.75
Specificity: 0.69
NPV: 0.73
ECE: 0.08
MCE: 0.18
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.62
AU-ROC: 0.94
AU-PRC: 0.65
AU-PRC Baaseline: 0.13
Accuracy: 0.85
Precision: 0.47
Recall: 0.92
Specificity: 0.84
NPV: 0.99
ECE: 0.15
MCE: 0.40
======= VALIDATION ========
BCE Loss: 1.31
AU-ROC: 0.79
AU-PRC: 0.77
AU-PRC Baaseline: 0.50
Accuracy: 0.72
Precision: 0.74
Recall: 0.69
Specificity: 0.76
NPV: 0.71
ECE: 0.11
MCE: 0.28
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.86
AU-ROC: 0.89
AU-PRC: 0.88
AU-PRC Baaseline: 0.49
Accuracy: 0.81
Precision: 0.78
Recall: 0.87
Specificity: 0.75
NPV: 0.86
ECE: 0.04
MCE: 0.08
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 2 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.18
AU-ROC: 0.75
AU-PRC: 0.33
AU-PRC Baaseline: 0.13
Accuracy: 0.68
Precision: 0.24
Recall: 0.68
Specificity: 0.68
NPV: 0.93
ECE: 0.30
MCE: 0.47
======= VALIDATION ========
BCE Loss: 1.04
AU-ROC: 0.84
AU-PRC: 0.85
AU-PRC Baaseline: 0.53
Accuracy: 0.76
Precision: 0.76
Recall: 0.79
Specificity: 0.72
NPV: 0.75
ECE: 0.06
MCE: 0.15
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.81
AU-ROC: 0.90
AU-PRC: 0.58
AU-PRC Baaseline: 0.13
Accuracy: 0.80
Precision: 0.38
Recall: 0.85
Specificity: 0.80
NPV: 0.97
ECE: 0.19
MCE: 0.45
======= VALIDATION ========
BCE Loss: 0.99
AU-ROC: 0.84
AU-PRC: 0.85
AU-PRC Baaseline: 0.53
Accuracy: 0.76
Precision: 0.76
Recall: 0.79
Specificity: 0.71
NPV: 0.76
ECE: 0.04
MCE: 0.08
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.68
AU-ROC: 0.93
AU-PRC: 0.64
AU-PRC Baaseline: 0.13
Accuracy: 0.83
Precision: 0.43
Recall: 0.88
Specificity: 0.83
NPV: 0.98
ECE: 0.15
MCE: 0.42
======= VALIDATION ========
BCE Loss: 1.43
AU-ROC: 0.81
AU-PRC: 0.82
AU-PRC Baaseline: 0.53
Accuracy: 0.72
Precision: 0.77
Recall: 0.69
Specificity: 0.77
NPV: 0.69
ECE: 0.16
MCE: 0.30
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.61
AU-ROC: 0.94
AU-PRC: 0.64
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.45
Recall: 0.93
Specificity: 0.83
NPV: 0.99
ECE: 0.14
MCE: 0.49
======= VALIDATION ========
BCE Loss: 1.82
AU-ROC: 0.79
AU-PRC: 0.79
AU-PRC Baaseline: 0.53
Accuracy: 0.72
Precision: 0.82
Recall: 0.60
Specificity: 0.86
NPV: 0.65
ECE: 0.20
MCE: 0.35
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 1.14
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.50
Accuracy: 0.80
Precision: 0.83
Recall: 0.76
Specificity: 0.84
NPV: 0.78
ECE: 0.09
MCE: 0.28
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 3 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.19
AU-ROC: 0.75
AU-PRC: 0.29
AU-PRC Baaseline: 0.13
Accuracy: 0.62
Precision: 0.22
Recall: 0.75
Specificity: 0.61
NPV: 0.94
ECE: 0.31
MCE: 0.55
======= VALIDATION ========
BCE Loss: 1.01
AU-ROC: 0.85
AU-PRC: 0.87
AU-PRC Baaseline: 0.53
Accuracy: 0.78
Precision: 0.76
Recall: 0.85
Specificity: 0.69
NPV: 0.80
ECE: 0.07
MCE: 0.17
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.85
AU-ROC: 0.89
AU-PRC: 0.53
AU-PRC Baaseline: 0.13
Accuracy: 0.74
Precision: 0.32
Recall: 0.88
Specificity: 0.72
NPV: 0.98
ECE: 0.21
MCE: 0.47
======= VALIDATION ========
BCE Loss: 1.00
AU-ROC: 0.85
AU-PRC: 0.87
AU-PRC Baaseline: 0.53
Accuracy: 0.77
Precision: 0.75
Recall: 0.84
Specificity: 0.69
NPV: 0.79
ECE: 0.07
MCE: 0.15
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.72
AU-ROC: 0.92
AU-PRC: 0.61
AU-PRC Baaseline: 0.13
Accuracy: 0.79
Precision: 0.38
Recall: 0.91
Specificity: 0.78
NPV: 0.98
ECE: 0.18
MCE: 0.49
======= VALIDATION ========
BCE Loss: 1.04
AU-ROC: 0.84
AU-PRC: 0.87
AU-PRC Baaseline: 0.53
Accuracy: 0.77
Precision: 0.78
Recall: 0.80
Specificity: 0.74
NPV: 0.76
ECE: 0.08
MCE: 0.13
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.60
AU-ROC: 0.94
AU-PRC: 0.68
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.44
Recall: 0.93
Specificity: 0.83
NPV: 0.99
ECE: 0.14
MCE: 0.46
======= VALIDATION ========
BCE Loss: 1.11
AU-ROC: 0.83
AU-PRC: 0.85
AU-PRC Baaseline: 0.53
Accuracy: 0.75
Precision: 0.79
Recall: 0.72
Specificity: 0.79
NPV: 0.71
ECE: 0.08
MCE: 0.14
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.95
AU-ROC: 0.87
AU-PRC: 0.83
AU-PRC Baaseline: 0.49
Accuracy: 0.80
Precision: 0.77
Recall: 0.85
Specificity: 0.75
NPV: 0.83
ECE: 0.06
MCE: 0.16
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 4 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.18
AU-ROC: 0.75
AU-PRC: 0.33
AU-PRC Baaseline: 0.13
Accuracy: 0.67
Precision: 0.24
Recall: 0.72
Specificity: 0.66
NPV: 0.94
ECE: 0.30
MCE: 0.48
======= VALIDATION ========
BCE Loss: 1.07
AU-ROC: 0.82
AU-PRC: 0.81
AU-PRC Baaseline: 0.52
Accuracy: 0.75
Precision: 0.78
Recall: 0.73
Specificity: 0.78
NPV: 0.72
ECE: 0.07
MCE: 0.11
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.83
AU-ROC: 0.89
AU-PRC: 0.56
AU-PRC Baaseline: 0.13
Accuracy: 0.79
Precision: 0.36
Recall: 0.85
Specificity: 0.78
NPV: 0.97
ECE: 0.20
MCE: 0.43
======= VALIDATION ========
BCE Loss: 1.09
AU-ROC: 0.82
AU-PRC: 0.84
AU-PRC Baaseline: 0.52
Accuracy: 0.72
Precision: 0.83
Recall: 0.58
Specificity: 0.87
NPV: 0.66
ECE: 0.10
MCE: 0.18
No improvement in Validation results
======= EPOCH 2.0 ========
BCE Loss: 0.71
AU-ROC: 0.92
AU-PRC: 0.59
AU-PRC Baaseline: 0.13
Accuracy: 0.82
Precision: 0.41
Recall: 0.89
Specificity: 0.81
NPV: 0.98
ECE: 0.15
MCE: 0.44
======= VALIDATION ========
BCE Loss: 1.18
AU-ROC: 0.81
AU-PRC: 0.84
AU-PRC Baaseline: 0.52
Accuracy: 0.75
Precision: 0.80
Recall: 0.69
Specificity: 0.82
NPV: 0.71
ECE: 0.10
MCE: 0.18
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.94
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.50
Accuracy: 0.80
Precision: 0.77
Recall: 0.85
Specificity: 0.75
NPV: 0.83
ECE: 0.05
MCE: 0.14

10. Running BEHRT

Below we integrate the implementation of BEHRT in our pipeline. We perform pre-procesing needed to run BEHRT model. https://github.com/deepmedicine/BEHRT

Few things to note before running BEHRT -

• The numerical values are binned into quantiles.
• BEHRT has recommended maximum number of events per sample to be 512. So feature selection is important so that number of events per sample does not exceed 512.
• The model is quite computationally heavy so it requires a GPU.

The output files for BEHRT will be saved in ./data/behrt/ folder

Please run below cell to to pre-process and run BEHRT on the selected cohort

behrt_instance = BEHRT_models(data_icu, diag_flag, proc_flag, out_flag, chart_flag, med_flag, False)

if data_icu:
    tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels = behrt_instance.tokenization()
else:
    tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels = behrt_instance.tokenization()

STARTING READING FILES.

100%|██████████| 5736/5736 [02:28<00:00, 38.58it/s]

FINISHED READING FILES. 

STARTING TOKENIZATION.

100%|██████████| 5736/5736 [06:21<00:00, 15.02it/s]

FINISHED TOKENIZATION. 

FINAL COHORT STATISTICS: 
1 Positive samples.
23 Negative samples.

8 Female samples.
16 Male samples.

17 WHITE samples.
2 BLACK/AFRICAN AMERICAN samples.
0 BLACK/CAPE VERDEAN samples.
0 UNKNOWN samples.
0 BLACK/AFRICAN samples.
1 HISPANIC/LATINO - PUERTO RICAN samples.
0 WHITE - RUSSIAN samples.
0 WHITE - EASTERN EUROPEAN samples.
0 ASIAN - CHINESE samples.
1 ASIAN samples.
0 OTHER samples.
1 HISPANIC OR LATINO samples.
0 ASIAN - ASIAN INDIAN samples.
0 WHITE - OTHER EUROPEAN samples.
1 UNABLE TO OBTAIN samples.
0 NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER samples.
0 HISPANIC/LATINO - CENTRAL AMERICAN samples.
0 HISPANIC/LATINO - GUATEMALAN samples.
0 PORTUGUESE samples.
0 PATIENT DECLINED TO ANSWER samples.
1 HISPANIC/LATINO - DOMINICAN samples.
0 HISPANIC/LATINO - HONDURAN samples.
0 BLACK/CARIBBEAN ISLAND samples.
0 ASIAN - KOREAN samples.
0 ASIAN - SOUTH EAST ASIAN samples.
0 WHITE - BRAZILIAN samples.
0 HISPANIC/LATINO - CUBAN samples.
0 AMERICAN INDIAN/ALASKA NATIVE samples.
0 HISPANIC/LATINO - COLUMBIAN samples.
0 HISPANIC/LATINO - SALVADORAN samples.
0 SOUTH AMERICAN samples.
0 HISPANIC/LATINO - MEXICAN samples.
0 MULTIPLE RACE/ETHNICITY samples.


8 Other samples.
13 Medicare samples.
3 Medicaid samples.

import numpy as np
tokenized_src.replace(np.nan, 0, inplace=True)
tokenized_age.replace(np.nan, 0, inplace=True)
tokenized_gender.replace(np.nan, 0, inplace=True)
tokenized_ethni.replace(np.nan, 0, inplace=True)
tokenized_ins.replace(np.nan, 0, inplace=True)

tokenized_labels

	0
0	0
1	0
2	0
3	0
4	0
5	0
6	0
7	0
8	0
9	0
10	0
11	0
12	0
13	0
14	1
15	0
16	0
17	0
18	0
19	0
20	0
21	0
22	0
23	0

train_behrt_instance = behrt_train.train_behrt(tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels)

Epoch n0
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TOTAL LOSS 0.6492533683776855
TRAIN 0.6880692839622498	0.972445011138916 secs

EVAL 0.6492533683776855	0.029590845108032227 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n1
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TOTAL LOSS 0.6152746677398682
TRAIN 0.6427515149116516	0.6085367202758789 secs

EVAL 0.6152746677398682	0.028502464294433594 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n2
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TOTAL LOSS 0.5831897258758545
TRAIN 0.6320257782936096	0.6558012962341309 secs

EVAL 0.5831897258758545	0.028824567794799805 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n3
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TRAIN 0.6086511611938477	0.602691650390625 secs

EVAL 0.5527118444442749	0.029767990112304688 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n4
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TOTAL LOSS 0.5237722396850586
TRAIN 0.5621075630187988	0.5786316394805908 secs

EVAL 0.5237722396850586	0.029163837432861328 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n5
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TOTAL LOSS 0.49637794494628906
TRAIN 0.5760729908943176	0.600928544998169 secs

EVAL 0.49637794494628906	0.028235435485839844 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n6
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TOTAL LOSS 0.4705328941345215
TRAIN 0.5437617897987366	0.6338632106781006 secs

EVAL 0.4705328941345215	0.02995133399963379 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n7
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TOTAL LOSS 0.44615495204925537
TRAIN 0.5126276016235352	0.588768482208252 secs

EVAL 0.44615495204925537	0.0295867919921875 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n8
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TOTAL LOSS 0.4233209490776062
TRAIN 0.4943127930164337	0.612755537033081 secs

EVAL 0.4233209490776062	0.029216289520263672 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n9
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TOTAL LOSS 0.40190085768699646
TRAIN 0.4910022020339966	0.6032013893127441 secs

EVAL 0.40190085768699646	0.02872180938720703 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n10
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TOTAL LOSS 0.381802499294281
TRAIN 0.460899293422699	0.6580772399902344 secs

EVAL 0.381802499294281	0.028850555419921875 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n11
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TOTAL LOSS 0.36295926570892334
TRAIN 0.45757803320884705	0.5803976058959961 secs

EVAL 0.36295926570892334	0.028995990753173828 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n12
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TOTAL LOSS 0.345333993434906
TRAIN 0.44571995735168457	0.6426436901092529 secs

EVAL 0.345333993434906	0.02844548225402832 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n13
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TOTAL LOSS 0.3288305401802063
TRAIN 0.42566561698913574	0.5831763744354248 secs

EVAL 0.3288305401802063	0.0300905704498291 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n14
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TOTAL LOSS 0.31338977813720703
TRAIN 0.40067505836486816	0.6559710502624512 secs

EVAL 0.31338977813720703	0.028939008712768555 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n15
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TOTAL LOSS 0.2989567518234253
TRAIN 0.3930733799934387	0.5799570083618164 secs

EVAL 0.2989567518234253	0.029539108276367188 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n16
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TOTAL LOSS 0.28549623489379883
TRAIN 0.3829631805419922	0.671839714050293 secs

EVAL 0.28549623489379883	0.029250383377075195 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n17
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TOTAL LOSS 0.2729647159576416
TRAIN 0.36835891008377075	0.5820581912994385 secs

EVAL 0.2729647159576416	0.028673648834228516 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n18
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TRAIN 0.36681169271469116	0.5732851028442383 secs

EVAL 0.2612842321395874	0.029007911682128906 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n19
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TOTAL LOSS 0.250369668006897
TRAIN 0.3551793098449707	0.5727436542510986 secs

EVAL 0.250369668006897	0.02890181541442871 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n20
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TOTAL LOSS 0.24017959833145142
TRAIN 0.35074421763420105	0.6220932006835938 secs

EVAL 0.24017959833145142	0.028767824172973633 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n21
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TOTAL LOSS 0.2306538075208664
TRAIN 0.3391158878803253	0.6026854515075684 secs

EVAL 0.2306538075208664	0.029915809631347656 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n22
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TOTAL LOSS 0.22174513339996338
TRAIN 0.33405840396881104	0.5828814506530762 secs

EVAL 0.22174513339996338	0.028567075729370117 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n23
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TOTAL LOSS 0.21343892812728882
TRAIN 0.31914401054382324	0.6001870632171631 secs

EVAL 0.21343892812728882	0.029213428497314453 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n24
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TOTAL LOSS 0.2056952714920044
TRAIN 0.3105507791042328	0.5471067428588867 secs

EVAL 0.2056952714920044	0.029172658920288086 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n25
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TOTAL LOSS 0.19846615195274353
TRAIN 0.3152894377708435	0.6329076290130615 secs

EVAL 0.19846615195274353	0.02953505516052246 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n26
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TOTAL LOSS 0.19170403480529785
TRAIN 0.29996955394744873	0.5492405891418457 secs

EVAL 0.19170403480529785	0.02935194969177246 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n27
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TOTAL LOSS 0.18539923429489136
TRAIN 0.3100718557834625	0.5516624450683594 secs

EVAL 0.18539923429489136	0.028885602951049805 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n28
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TOTAL LOSS 0.17948248982429504
TRAIN 0.2961326837539673	0.5510234832763672 secs

EVAL 0.17948248982429504	0.02842259407043457 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n29
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TOTAL LOSS 0.1739269196987152
TRAIN 0.28419169783592224	0.564349889755249 secs

EVAL 0.1739269196987152	0.02924823760986328 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n30
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TOTAL LOSS 0.16872766613960266
TRAIN 0.2896280884742737	0.5432572364807129 secs

EVAL 0.16872766613960266	0.02950906753540039 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n31
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TOTAL LOSS 0.16384007036685944
TRAIN 0.2840370237827301	0.5257735252380371 secs

EVAL 0.16384007036685944	0.028269052505493164 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n32
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TOTAL LOSS 0.15924817323684692
TRAIN 0.28724777698516846	0.5699069499969482 secs

EVAL 0.15924817323684692	0.029603004455566406 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n33
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TOTAL LOSS 0.15493935346603394
TRAIN 0.27870267629623413	0.5863687992095947 secs

EVAL 0.15493935346603394	0.029551982879638672 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n34
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TOTAL LOSS 0.15089932084083557
TRAIN 0.2681189179420471	0.5397496223449707 secs

EVAL 0.15089932084083557	0.028810739517211914 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n35
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TOTAL LOSS 0.14709904789924622
TRAIN 0.25979605317115784	0.6089081764221191 secs

EVAL 0.14709904789924622	0.029154539108276367 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n36
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TRAIN 0.2636283040046692	0.5471689701080322 secs

EVAL 0.14347349107265472	0.02971792221069336 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n37
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TOTAL LOSS 0.14007611572742462
TRAIN 0.27787402272224426	0.5458102226257324 secs

EVAL 0.14007611572742462	0.028585195541381836 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n38
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TOTAL LOSS 0.13688987493515015
TRAIN 0.25816628336906433	0.6164150238037109 secs

EVAL 0.13688987493515015	0.02909064292907715 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n39
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TOTAL LOSS 0.13389405608177185
TRAIN 0.2615320384502411	0.5720400810241699 secs

EVAL 0.13389405608177185	0.02969074249267578 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n40
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TOTAL LOSS 0.1310836672782898
TRAIN 0.2613388001918793	0.5526258945465088 secs

EVAL 0.1310836672782898	0.029985427856445312 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n41
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TOTAL LOSS 0.12842980027198792
TRAIN 0.24899952113628387	0.5681211948394775 secs

EVAL 0.12842980027198792	0.02883291244506836 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n42
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TOTAL LOSS 0.12595093250274658
TRAIN 0.253766268491745	0.6252055168151855 secs

EVAL 0.12595093250274658	0.029779911041259766 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n43
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TOTAL LOSS 0.12360724806785583
TRAIN 0.2564980983734131	0.6413474082946777 secs

EVAL 0.12360724806785583	0.029520273208618164 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n44
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TOTAL LOSS 0.12137870490550995
TRAIN 0.2356296181678772	0.5995159149169922 secs

EVAL 0.12137870490550995	0.029970645904541016 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n45
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TOTAL LOSS 0.11927545070648193
TRAIN 0.24498263001441956	0.6590576171875 secs

EVAL 0.11927545070648193	0.029950857162475586 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n46
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TOTAL LOSS 0.11729846894741058
TRAIN 0.2497648000717163	0.647169828414917 secs

EVAL 0.11729846894741058	0.02938222885131836 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n47
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TOTAL LOSS 0.11541888117790222
TRAIN 0.24764384329319	0.6634378433227539 secs

EVAL 0.11541888117790222	0.029593229293823242 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n48
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TOTAL LOSS 0.11365646123886108
TRAIN 0.2364962249994278	0.6482224464416504 secs

EVAL 0.11365646123886108	0.028989315032958984 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n49
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TOTAL LOSS 0.11198864877223969
TRAIN 0.23867680132389069	0.5915560722351074 secs

EVAL 0.11198864877223969	0.03008580207824707 secs

** ** * Saving fine - tuned model ** ** * 
Loading succesfull
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TOTAL LOSS 0.11390753835439682
tensor([[0.1069],
        [0.1104],
        [0.1024],
        [0.1128],
        [0.1128],
        [0.1006]])
tensor(0.)
tensor(nan)
tensor(0.)
tensor(0.)

EVALUATION AS STANDALONE MODULE

Below cell shows an exaple of how evaluation module can be used as a standalone module.

evaluation.Loss class can be instantiated and model output and ground truth can be passed to it to obtain results.

In the example below we captured model output and ground truth in a file and used that file to read the data.

In function definition loss(prob,truth,logits,False)

prob -> List of Output predicted probabilities of case being positive

truth -> List of ground truth labels

logits -> List of logits obtained from last fully connected layer before applying softmax.sigmoid function in the model.

if torch.cuda.is_available():
    device='cuda:0'
#device='cpu'
loss=evaluation.Loss(device,acc=True,ppv=True,sensi=True,tnr=True,npv=True,auroc=True,aurocPlot=True,auprc=True,auprcPlot=True,callb=True,callbPlot=True)
with open("./data/output/outputDict", 'rb') as fp:
    outputDict=pickle.load(fp)
prob=list(outputDict['Prob'])
truth=list(outputDict['Labels'])
logits=list(outputDict['Logits'])
#print(torch.tensor(prob))
print("======= TESTING ========")
loss(prob,truth,logits,train=False,standalone=True)

======= TESTING ========
BCE Loss: 0.94
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.50
Accuracy: 0.80
Precision: 0.77
Recall: 0.85
Specificity: 0.75
NPV: 0.83
ECE: 0.05
MCE: 0.14

11. FAIRNESS EVALUATION

In train and testing step we save output files in ./data/output/ folder.

This file conatins list of demographic variables included in training and testing of the model.

It also contains the ground truth labels and predicted probability for each sample.

We use the above saved data to perform fairness evaluation of the results obtained from model testing.

This module can be used as stand-alone module also.

Please create a file that contains predicted probabilites form the last sigmoid layer in column named Prob and ground truth labels for each sample in column named Labels.

fairness.fairness_evaluation(inputFile='outputDict',outputFile='fairnessReport')

	sensitive_attribute	group	tp	tn	fp	fn	tpr	tnr	fpr	fnr	pr	nr	accuracy
0	ethnicity	AMERICAN INDIAN/ALASKA NATIVE	0	1	1	0	nan	0.500000	0.500000	nan	0.500000	0.500000	0.500000
1	ethnicity	ASIAN	7	4	2	3	0.700000	0.666667	0.333333	0.300000	0.562500	0.437500	0.687500
2	ethnicity	ASIAN - ASIAN INDIAN	2	1	1	0	1.000000	0.500000	0.500000	0.000000	0.750000	0.250000	0.750000
3	ethnicity	ASIAN - CHINESE	11	4	1	0	1.000000	0.800000	0.200000	0.000000	0.750000	0.250000	0.937500
4	ethnicity	ASIAN - KOREAN	1	0	1	0	1.000000	0.000000	1.000000	0.000000	1.000000	0.000000	0.500000
5	ethnicity	ASIAN - SOUTH EAST ASIAN	4	3	0	0	1.000000	1.000000	0.000000	0.000000	0.571429	0.428571	1.000000
6	ethnicity	BLACK/AFRICAN	1	1	1	0	1.000000	0.500000	0.500000	0.000000	0.666667	0.333333	0.666667
7	ethnicity	BLACK/AFRICAN AMERICAN	101	101	23	20	0.834711	0.814516	0.185484	0.165289	0.506122	0.493878	0.824490
8	ethnicity	BLACK/CAPE VERDEAN	7	4	0	0	1.000000	1.000000	0.000000	0.000000	0.636364	0.363636	1.000000
9	ethnicity	BLACK/CARIBBEAN ISLAND	0	7	0	0	nan	1.000000	0.000000	nan	0.000000	1.000000	1.000000
10	ethnicity	HISPANIC OR LATINO	1	2	2	0	1.000000	0.500000	0.500000	0.000000	0.600000	0.400000	0.600000
11	ethnicity	HISPANIC/LATINO - CENTRAL AMERICAN	0	1	0	0	nan	1.000000	0.000000	nan	0.000000	1.000000	1.000000
12	ethnicity	HISPANIC/LATINO - DOMINICAN	5	5	1	0	1.000000	0.833333	0.166667	0.000000	0.545455	0.454545	0.909091
13	ethnicity	HISPANIC/LATINO - GUATEMALAN	0	3	0	0	nan	1.000000	0.000000	nan	0.000000	1.000000	1.000000
14	ethnicity	HISPANIC/LATINO - HONDURAN	1	0	0	0	1.000000	nan	nan	0.000000	1.000000	0.000000	1.000000
15	ethnicity	HISPANIC/LATINO - MEXICAN	0	0	1	0	nan	0.000000	1.000000	nan	1.000000	0.000000	0.000000
16	ethnicity	HISPANIC/LATINO - PUERTO RICAN	14	9	1	3	0.823529	0.900000	0.100000	0.176471	0.555556	0.444444	0.851852
17	ethnicity	HISPANIC/LATINO - SALVADORAN	2	0	0	0	1.000000	nan	nan	0.000000	1.000000	0.000000	1.000000
18	ethnicity	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	0	1	0	0	nan	1.000000	0.000000	nan	0.000000	1.000000	1.000000
19	ethnicity	OTHER	15	21	6	9	0.625000	0.777778	0.222222	0.375000	0.411765	0.588235	0.705882
20	ethnicity	PATIENT DECLINED TO ANSWER	0	2	2	1	0.000000	0.500000	0.500000	1.000000	0.400000	0.600000	0.400000
21	ethnicity	PORTUGUESE	5	3	4	0	1.000000	0.428571	0.571429	0.000000	0.750000	0.250000	0.666667
22	ethnicity	UNABLE TO OBTAIN	12	7	1	0	1.000000	0.875000	0.125000	0.000000	0.650000	0.350000	0.950000
23	ethnicity	UNKNOWN	86	49	20	12	0.877551	0.710145	0.289855	0.122449	0.634731	0.365269	0.808383
24	ethnicity	WHITE	450	415	153	82	0.845865	0.730634	0.269366	0.154135	0.548182	0.451818	0.786364
25	ethnicity	WHITE - BRAZILIAN	0	1	0	0	nan	1.000000	0.000000	nan	0.000000	1.000000	1.000000
26	ethnicity	WHITE - EASTERN EUROPEAN	1	1	0	0	1.000000	1.000000	0.000000	0.000000	0.500000	0.500000	1.000000
27	ethnicity	WHITE - OTHER EUROPEAN	23	11	5	5	0.821429	0.687500	0.312500	0.178571	0.636364	0.363636	0.772727
28	ethnicity	WHITE - RUSSIAN	13	15	4	1	0.928571	0.789474	0.210526	0.071429	0.515152	0.484848	0.848485
29	gender	F	264	263	96	64	0.804878	0.732591	0.267409	0.195122	0.524017	0.475983	0.767103
30	gender	M	498	409	134	72	0.873684	0.753223	0.246777	0.126316	0.567835	0.432165	0.814915
31	age_binned	20-30	1	11	1	0	1.000000	0.916667	0.083333	0.000000	0.153846	0.846154	0.923077
32	age_binned	30-40	6	17	5	3	0.666667	0.772727	0.227273	0.333333	0.354839	0.645161	0.741935
33	age_binned	40-50	33	37	9	6	0.846154	0.804348	0.195652	0.153846	0.494118	0.505882	0.823529
34	age_binned	50-60	84	108	28	24	0.777778	0.794118	0.205882	0.222222	0.459016	0.540984	0.786885
35	age_binned	60-70	201	152	57	28	0.877729	0.727273	0.272727	0.122271	0.589041	0.410959	0.805936
36	age_binned	70-80	223	187	68	40	0.847909	0.733333	0.266667	0.152091	0.561776	0.438224	0.791506
37	age_binned	80-90	182	131	52	26	0.875000	0.715847	0.284153	0.125000	0.598465	0.401535	0.800512
38	age_binned	90-100	32	29	10	9	0.780488	0.743590	0.256410	0.219512	0.525000	0.475000	0.762500

12. MODEL CALLIBRATION

Please run below cell if you want to callibrate predicted probabilites of the model on test data. It will use the output saved during the testing of the model.

The file is saved in ./data/output/.

This module can be used as stand-alone module also.

Please create a file that contain predicted logits form the last fully connected layer in column named Logits and
ground truth labels for each sample in a column named Labels.

callibrate_output.callibrate(inputFile='outputDict',outputFile='callibratedResults')

BEFORE CALLIBRATION
BCE Loss: 0.94
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.50
Accuracy: 0.80
Precision: 0.77
Recall: 0.85
Specificity: 0.75
NPV: 0.83
ECE: 0.05
MCE: 0.14
AFTER CALLIBRATION
BCE Loss: 0.92
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.50
Accuracy: 0.80
Precision: 0.77
Recall: 0.85
Specificity: 0.75
NPV: 0.83
ECE: 0.04
MCE: 0.13